Dimensional Models for Hadoop and Big Data

 

Benefits Of DimensionalModels for Hadoop and Big Data

 

Dimensional modeling has gone through a disruptive technological change in the past few years. Many people question its relevance in the age of Hadoop and big data. It’s an impression that it is an old-fashioned technique. But even after recent upheavals in technology, it is a relevant practice.

What Is Dimensional Modeling?

Dimensional Modeling (DM) is a data structure technique developed by Ralph Kimbell. It includes a set of techniques, methods, and concepts for use in the data warehouse.

It is used to optimize the database for quick data retrieval and consists of dimension and fact tables. A dimensional model in the data warehouse is designed to summarize, read, and analyze numeric information like counts, balances, values, weights, etc., in a data warehouse.

On the other hand, the relation model is optimized for updating, adding, and deleting data in a real-time Online Transaction System. These models have a unique way to store data and offer many advantages too.

For example, in the relational model, the ER models and normalization help reduce redundancy in the data. Similarly, in dimensional data modeling, data is arranged in such a way that it is easier to generate reports and retrieve information.  Usually, dimensional models are helpful in data warehouse systems and not a good fit for relational systems.

Benefits Of Dimensional Models for Hadoop and Big Data

Dimensional modeling can apply to any data practitioner, be it a financial analyst or a data architect. It is relevant for anyone who spends time manipulating data. Whether you need to make reports, dashboards, or basic forecasts, dimensional modeling is applicable in many ways. Let’s explore how it fits into the big data Hadoop ecosystem.

Data in context offer robust analysis

A dimensional model tackles the issue of analytical decision-making. It takes the numbers and depicts them with characteristics about the event that generated them. It highlights the qualities, characteristics, facets, and features of basically everything in the data collection situation. This helps understand the patterns and information hidden in data. That’s why it is used by analysts and data scientists.

Extensibility on Hadoop and other data frameworks

 

Facts and dimensions are the main concepts encompassing dimensional modeling Dimensions and the surrogate key, which identifies each member of a dimension, are independent of their source system. As each dimension is separate from the source system, it makes the data warehouse modular.

Dimensional models are scalable and can easily accommodate new data. You can change the existing tables by adding new data rows or executing SQL alter table commands. There is no need to reprogram the queries or applications sitting on top of the data warehouse to accommodate the changes.

Helps focus on relevant data

You can’t escape data cleansing and transformation in big data analysis. You will have to put in efforts to ensure the quality, consistency of data that is standardized for analysis. Data modeling helps businesses focus on the data that matters most to them.

Dimensional models offer value to the business

Dimensional models are comparatively cheaper and offer real tangible values to the business. With the help of dimensional models, you can show the business processes in an integrated manner. When needed, you can perform data cleansing and transformation work for an advanced data analysis.

Makes data easy to understand

Technological evolutions have led to the rise of a complex data landscape, including unstructured, semi-structured data, sensor analytics, text analytics, and web statistics. A dimensional model help understand the data in the form of an easily comprehendible table or graph.  It speeds up other related processes like report creation and calculations and helps maintain consistency.

Most of the time spent by data scientists goes into data wrangling, cleaning, and organizing to obtain a clean dataset. This structure is easy to attain through dimensional modeling.

Dimensional modeling is useful for many business problems, regardless of the end user’s technical knowledge. If you have learned how to design your model in Hadoop training, it can save a lot of your time and effort in cleaning and organizing data and make valuable insights.

Even in the changing data analytics age, dimensional modeling will continue to simplify data representation and allow access to the mammoth of data generated every day and focus on the most valuable information. Hadoop bootcamps can help you learn how to utilize data modeling for Hadoop and big data.